docs/DATASET_SCHEMA.md

# TacRich-Manip LeRobot v3 — complete schema

This document is the normative field reference for TacRich-Manip LeRobot task
repositories. `meta/info.json` remains the machine-readable source of shapes,
dtypes, FPS, and path templates. The dataset card summarizes the same fields;
this file expands every key, coordinate frame, unit, order, and storage detail.

## Shape and runtime conventions

- Logical image shapes in `meta/info.json` use `[height,width,channel]`.
- LeRobot commonly returns decoded RGB as PyTorch `[channel,height,width]`
  float32 in `[0,1]`; this runtime layout is not a change to the stored schema.
- Scalar features are declared with shape `[1]` but are serialized as scalar
  Parquet values and may be returned as zero-dimensional tensors.
- `float32` vectors have expected quantization at approximately 1e-7 relative
  precision. Source timestamps are stored as Parquet `float64`.
- Quaternion order is always `[qx,qy,qz,qw]`; positions are `[x,y,z]`.

## Complete per-frame feature dictionary

### Visual and tactile observations

| Key | `info.json` dtype | Logical shape | Storage and value definition |
| --- | --- | ---: | --- |
| `observation.images.cam_front` | `video` | `[480,640,3]` | Front RGB at time `t`; MP4 AV1, 40 Hz nominal, HWC RGB/uint8-equivalent before loader conversion |
| `observation.images.cam_side` | `video` | `[480,640,3]` | Side RGB at time `t`; same format |
| `observation.images.cam_fisheye` | `video` | `[480,640,3]` | Gripper fisheye RGB at time `t`; same format |
| `observation.depth.cam_front` | `image` | `[480,640,1]` | Front depth at time `t`; lossless 16-bit PNG, uint16, millimetres, stored in the data Parquet image struct |
| `observation.tactile` | `float32` | `[2,32,58]` | PA-STE response indexed `[finger,row,column]`; finger 0 = left/NPZ key `0`, finger 1 = right/NPZ key `1` |

RGB video keys are not columns in `data/**/*.parquet`; LeRobot resolves them
through the video ranges in `meta/episodes/**/*.parquet`. This is normal v3
layout, not missing conversion. Depth is deliberately a Parquet image field so
the 16-bit PNG bytes remain lossless. Tactile response is dimensionless after
sensor calibration and must not be interpreted as newtons without a separate
force calibration.

### Robot observations and actions

| Key | Storage dtype | Shape | Definition |
| --- | --- | ---: | --- |
| `observation.joint_position` | float32 | `[7]` | Current robot joints `[arm_j1,...,arm_j7]`, degrees; all zeros for UMI when joints are unavailable |
| `observation.ee_pose` | float32 | `[7]` | Current flange/TCP pose `[x,y,z,qx,qy,qz,qw]`; position metres, orientation unit quaternion |
| `observation.gripper_distance` | float32 scalar | `[1]` | Current opening distance, millimetres |
| `observation.state` | float32 | `[15]` | Current joint + flange/TCP + gripper state; exact index table below |
| `observation.state_gripper` | float32 | `[10]` | Current gripper-tip/TCP pose in rotation-6D form plus opening; exact index table below |
| `action` | float32 | `[8]` | Absolute flange/TCP target plus gripper target; exact index table below |
| `action_gripper` | float32 | `[10]` | Absolute gripper-tip/TCP target in rotation-6D form plus target opening |

#### `observation.state` indices

| Index | Name | Unit | Frame / meaning |
| ---: | --- | --- | --- |
| 0–6 | `arm_j1` … `arm_j7` | degree | Current robot joints; UMI zero-fill only |
| 7 | `flange_x_m` | m | Current flange/TCP X |
| 8 | `flange_y_m` | m | Current flange/TCP Y |
| 9 | `flange_z_m` | m | Current flange/TCP Z |
| 10 | `flange_qx` | 1 | Quaternion X, XYZW order |
| 11 | `flange_qy` | 1 | Quaternion Y |
| 12 | `flange_qz` | 1 | Quaternion Z |
| 13 | `flange_qw` | 1 | Quaternion W |
| 14 | `gripper_distance_mm` | mm | Current opening distance |

#### `action` indices

| Index | Name | Unit | Frame / meaning |
| ---: | --- | --- | --- |
| 0 | `target_flange_x_m` | m | Absolute target X |
| 1 | `target_flange_y_m` | m | Absolute target Y |
| 2 | `target_flange_z_m` | m | Absolute target Z |
| 3 | `target_flange_qx` | 1 | Target quaternion X, XYZW order |
| 4 | `target_flange_qy` | 1 | Target quaternion Y |
| 5 | `target_flange_qz` | 1 | Target quaternion Z |
| 6 | `target_flange_qw` | 1 | Target quaternion W |
| 7 | `target_gripper_distance_mm` | mm | Target opening distance |

#### `observation.state_gripper` and `action_gripper` indices

| Index | State name | Action name | Unit | Definition |
| ---: | --- | --- | --- | --- |
| 0 | `tip_x_m` | `target_tip_x_m` | m | Gripper-tip/TCP X |
| 1 | `tip_y_m` | `target_tip_y_m` | m | Gripper-tip/TCP Y |
| 2 | `tip_z_m` | `target_tip_z_m` | m | Gripper-tip/TCP Z |
| 3 | `R00` | `target_R00` | 1 | First rotation-matrix column, row 0 |
| 4 | `R10` | `target_R10` | 1 | First column, row 1 |
| 5 | `R20` | `target_R20` | 1 | First column, row 2 |
| 6 | `R01` | `target_R01` | 1 | Second column, row 0 |
| 7 | `R11` | `target_R11` | 1 | Second column, row 1 |
| 8 | `R21` | `target_R21` | 1 | Second column, row 2 |
| 9 | `gripper_distance_mm` | `target_gripper_distance_mm` | mm | Opening distance |

Rotation-6D is therefore `concat(R[:,0], R[:,1]) =
[R00,R10,R20,R01,R11,R21]`. A consumer may reconstruct an orthonormal matrix
by Gram–Schmidt normalization of the two stored columns.

### Time, episode, and indexing keys

| Key | Parquet dtype | Shape | Definition |
| --- | --- | ---: | --- |
| `observation.timestamp` | float64 | `[1]` | Aligned raw CSV timestamp, Unix seconds |
| `observation.source_timestamp_tactile` | float64 | `[1]` | Tactile source timestamp; explicit CSV value, otherwise filename timestamp, otherwise aligned timestamp |
| `observation.source_timestamp_proprio` | float64 | `[1]` | Proprioception source timestamp; explicit CSV value, otherwise aligned timestamp |
| `observation.source_timestamp_vision` | float64 | `[1]` | Front RGB source timestamp; explicit CSV value, otherwise filename timestamp, otherwise aligned timestamp |
| `timestamp` | float32 | `[1]` | LeRobot relative time in seconds: `frame_index / fps` |
| `frame_index` | int64 | `[1]` | Zero-based frame number within one episode |
| `episode_index` | int64 | `[1]` | Zero-based episode ID across the dataset |
| `index` | int64 | `[1]` | Zero-based global row ID across all episodes |
| `task_index` | int64 | `[1]` | Index into `meta/tasks.parquet` |

Do not use `timestamp` as wall-clock time. Conversely, do not subtract large
Unix timestamps after a loader has cast them to float32: epoch-scale float32
has coarse resolution. Read the Parquet float64 values or subtract in float64
first when precise sensor synchronization is required.

## Coordinate frames and fixed flange-to-tip transform

Let `B` be the robot base, `F` the flange frame, and `G` the published
gripper-tip/TCP frame. Homogeneous transforms use the convention `T_A_B` = pose
of frame B expressed in frame A. `T_F_G` is the same transform elsewhere named
`T_flange_gripper`. Teleoperation uses

```text
T_B_G = T_B_F @ T_F_G
```

with the exact configured values

```text
translation_F_G_m = [0.0, 0.0, 0.2]
yaw_F_G_deg       = +40.0
cos(40 deg)       = 0.7660444431
sin(40 deg)       = 0.6427876097

T_F_G =
[[ 0.7660444431, -0.6427876097, 0.0, 0.0 ],
 [ 0.6427876097,  0.7660444431, 0.0, 0.0 ],
 [ 0.0,           0.0,           1.0, 0.2 ],
 [ 0.0,           0.0,           0.0, 1.0 ]]

T_G_F = inverse(T_F_G) =
[[ 0.7660444431,  0.6427876097, 0.0,  0.0 ],
 [-0.6427876097,  0.7660444431, 0.0,  0.0 ],
 [ 0.0,           0.0,           1.0, -0.2 ],
 [ 0.0,           0.0,           0.0,  1.0 ]]
```

Equivalently:

```text
p_B_G = p_B_F + R_B_F @ [0,0,0.2]
R_B_G = R_B_F @ Rz(+40 degrees)
```

The translation is in the flange frame, so it must be rotated by the current
flange orientation. `observation.state` contains joints plus `T_B_F`;
`observation.state_gripper` omits joints and contains `T_B_G` as position +
rotation-6D. The same distinction applies to `action` and `action_gripper`.

## Collection-method action semantics

### Teleoperation

- State poses are absolute in the robot-base frame.
- `action[t]` is the migrated, applied absolute flange command from raw
  `arm_target_*` at aligned row `t`; only those columns are read.
- Raw `action_delta_*` is retained in the raw archive as provenance but is not
  read by either LeRobot or AVAloha conversion.
- `action_gripper[t]` is obtained from the same target with `T_B_G = T_B_F @
  T_F_G`; no independent zero or second clamp is applied.
- Commanded action and `state[t+1]` should follow the same trajectory, but they
  are not mathematical equality because command look-ahead, robot dynamics,
  controller filtering, and sensor latency are real.

### UMI

Let `T_0` be the first valid TCP pose of the episode:

```text
T_local_i = inverse(T_0) @ T_raw_i
p_local_i = R_0^T @ (p_i - p_0)
R_local_i = R_0^T @ R_i
```

The first valid state is zero translation with identity rotation. The action at
`t` is the next observed local TCP pose; the final action repeats the final
state. Joint fields are zero-filled because the UMI source has no robot joints.

## LeRobot v3 files and metadata keys

| File or directory | Complete purpose |
| --- | --- |
| `meta/info.json` | `codebase_version`, `robot_type`, totals, FPS, splits, chunk/file sizes, path templates, and all feature dtype/shape/name declarations |
| `meta/stats.json` | Global min/max/mean/std/count and quantiles used for normalization/audit |
| `meta/tasks.parquet` | Task strings and their integer `task_index` values |
| `meta/task_sources.parquet` | Release provenance: logical task, physical source name, collection method, main/test role, episode count, frame count, and duration at 40 Hz |
| `meta/episodes/chunk-*/file-*.parquet` | Per-episode length/tasks, global data bounds, data shard IDs, video shard/time ranges, and episode statistics |
| `data/chunk-*/file-*.parquet` | All non-video per-frame keys listed above |
| `videos/<video-key>/chunk-*/file-*.mp4` | AV1 RGB frames; multiple episodes may share a v3 video shard |

## Exact-depth loading note

The PNG bytes are uint16 and were verified lossless against source depth. With
some torchvision versions, `LeRobotDataset` returns a signed `torch.int16`
depth tensor because PIL mode `I;16` is passed through `ToTensor`. The bit
pattern is still exact; recover it before numeric use:

```python
depth = sample["observation.depth.cam_front"].cpu().numpy().squeeze()
if depth.dtype == np.int16:
    depth = depth.view(np.uint16)
else:
    depth = depth.astype(np.uint16, copy=False)
```

The provided `examples/visualize_episode.py` performs this conversion and does
not silently rescale depth to 8-bit.

**Depth-statistics note:** In this release, LeRobot's generic image-statistics path recorded depth as three-channel normalized [0,1] image statistics. Those `meta/stats.json` depth values are not metric millimetre statistics and must not normalize uint16 depth. The stored PNG values and the provided visualizer remain exact.